Knowledge Graph Insights

Panos Alexopoulos Any knowledge graph or other semantic artifact must be modeled before it's built. Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data." The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome. We talked about: his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc. the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI the role of semantic modeling in RAG and other hybrid AI architectures a brief overview of data modeling as a practice how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc. the role of semantic modeling and symbolic AI in emerging hybrid AI architectures the importance of defining the notion of "autonomy" as AI agents emerge Panos' bio Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world. Connect with Panos online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ENothdlfYGA Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers. Interview transcript Larry: Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days. Panos: Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning,

Mike Pool Mike Pool sees irony in the fact that semantic-technology practitioners struggle to use the word "semantics" in ways that meaningfully advance conversations about their knowlege-representation work. In a recent LinkedIn post, Mike even proposed a moratorium on the use of the word. We talked about: his multi-decade career in knowledge representation and ontology practice his opinion that we might benefit from a moratorium on the term "semantics" the challenges in pinning down the exact scope of semantic technology how semantic tech permits reusability and enables scalability the balance in semantic practice between 1) ascribing meaning in tech architectures independent of its use in applications and 2) considering end-use cases the importance of staying domain-focused as you do semantic work how to stay pragmatic in your choice of semantic methods how reification of objects is not inherently semantic but does create a framework for discovering meaning how to understand and capture subtle differences in meaning of seemingly clear terms like "merger" or "customer" how LLMs can facilitate capturing meaning Mike's bio Michael Pool works in the Office of the CTO at Bloomberg, where he is working on a tool to create and deploy ontologies across the firm. Previously, he was a principal ontologist on the Amazon Product Knowledge team, and has also worked to deploy semantic technologies/approaches and enterprise knowledge graphs at a number of big banks in New York City. Michael also spent a couple of years on the famous Cyc project and has evaluated knowledge representation technologies for DARPA. He has also worked on tooling to integrate probabilistic and semantic models and oversaw development of an ontology to support a consumer-facing semantic search engine. He lives in New York City and loves to run around in circles in Central Park. Connect with Mike online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/JlJjBWGwSDg Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 22. The word "semantics" is often used imprecisely by semantic-technology practitioners. It can describe a wide array of knowledge-representation practices, from simple glossaries and taxonomies to full-blown enterprise ontologies, any of which may be summarized in a conversation as "semantics." Mike Pool thinks that this dynamic - using a word that lacks precise meaning while assuming that it communicates a lot - may justify a moratorium on the use of the term. Interview transcript Larry: Hi everyone, welcome to episode number 22 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Mike Pool. Mike is a longtime ontologist, a couple of decades plus. He recently took a position at Bloomberg. But he made this really provocative post on LinkedIn lately that I want to flesh out today, and we'll talk more about that throughout the rest of the show. Welcome, Mike, tell the folks a little bit more about what you're up to these days. Mike: Hey, thank you, Larry. Yeah. As you noted, I've just taken a position with Bloomberg and for these many years that you alluded to, I've been very heavily focused on building, doing knowledge representation in general. In the last let's say decade or so I've been particularly focused on using ontologies and knowledge graphs in large banks, or large organizations at least, to help organize disparate data, to make it more accessible, breakdown data silos, et cetera. It's particularly relevant in the finance industry where things can be sliced and diced in so many different ways. I find there's a really important use case in the financial space but in large organizations in general, in my opinion, for using ontology. So that's a lot of what I've been thinking about, to make that more accessible to the organization and to help them build these ontologies and utilize th...

Margaret Warren As a 10-year-old photographer, Margaret Warren would jot down on the back of each printed photo metadata about who took the picture, who was in it, and where it was taken. Her interest in image metadata continued into her adult life, culminating the creation of ImageSnippets, a service that lets anyone add linked open data descriptions to their images. We talked about: her work to make images more discoverable with metadata connected via a knowledge graph how her early childhood history as a metadata strategist, her background in computing technology, and her personal interest in art and a photography shows up in her product, ImageSnippets her takes on the basics of metadata strategy and practice the many types of metadata: descriptive, administrative, technical, etc. the role of metadata in the new AI world some of the good and bad reasons that social media platforms might remove metadata from images privacy implications of metadata in social media the linked data principles that she applies in ImageSnippets and how they're managed in the product's workflow her wish that CMSs and social media platforms would not strip the metadata from images as they ingest them the lightweight image ontology that underlies her ImageSnippets product her prediction that the importance of metadata that supports provenance, demonstrates originality, and sets context will continue to grow in the future Margaret's bio Margaret Warren is a technologist, researcher and artist/content creator. She is the founder and CEO of Metadata Authoring Systems whose mission is to make the most obscure images on the web findable, and easily accessible by describing and preserving them in the most precise ways possible. To assist with this mission, she is the creator of a system called, ImageSnippets which can be used by anyone to build linked data descriptions of images into graphs. She is also a research associate with the Florida Institute of Human and Machine Cognition, one of the primary organizers of a group called The Dataworthy Collective and is a member of the IPTC (International Press and Telecommunications Council) photo-metadata working group and the Research Data Alliance charter on Collections as Data. As a researcher, Margaret's primary focus is at the intersection of semantics, metadata, knowledge representation and information science particularly around visual content, search and findability. She is deeply interested in how people describe what they experience visually and how to capture and formalize this knowledge into machine readable structures. She creates tools and processes for humans but augmented by machine intelligence. Many of these tools are useful for unifying the many types of metadata and descriptions of images - including the very important context element - into ontology infused knowledge graphs. Her tools can be used for tasks as advanced as complex domain modeling but can also facilitate image content to be shared and published while staying linked to it's metadata across workflows. Learn more and connect with Margaret online LinkedIn Patreon Bluesky Substack ImageSnippets Metadata Authoring Systems personal and art site IPTC links IPTC Photo Metadata Software that supports IPTC Photo Metadata Get IPTC Photo Metadata Browser extensions for IPTC Photo Metadata Resource not mentioned in podcast (but very useful for examining structured metadata in web pages) OpenLink Structured Data Sniffer (OSDS) Video Here’s the video version of our conversation: https://youtu.be/pjoAAq5zuRk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 21. Nowadays, we are all immersed in a deluge of information and media, especially images. The real value of these images is captured in the metadata about them. Without information about the history of an image, its technical details,

Jans Aasman Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit. Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies. We talked about: his knowledge graph and neuro-symbolic work as the CEO of Franz the crucial role of a visionary knowledge graph champion in KG adoption in enterprises the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs how gen AI lets him do things he couldn't have imagined five years ago the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI" the power of "magic predicates" a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions his strong belief in keeping humans in the loop in AI systems his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI" his take on the development of reasoning capabilities of LLMs how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders Jans' bio Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide. Connect with Jans online LinkedIn email: ja at franz dot com Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=SZBZxC8S1Uk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit. Interview transcript Larry: Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days. Jans: We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of. Larry: Who? Jans: These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies. Larry:

Juan Sequeda Knowledge graph technology has been around for decades. The benefits so far accruing to only a few big enterprises and tech companies. Juan Sequeda sees large language models as a critical enabler for the broader adoption of KGs. With their capacity to accelerate the acquisition and use of valuable business knowledge, LLMs offer a path to a better return on your enterprise's investment in semantics. We talked about: his work data.world as Principal scientist and the head of the AI lab at data.world the new discovery and knowledge-acquisition capabilities that LLMs give knowledge engineers a variety of business benefits that unfold from these new capabilities the payoff of investing in semantics and knowledge: "one plus one is greater than two" how semantic understanding and the move from a data-first world to a knowledge-first world helps businesses make better decisions and become more efficient the pendulum swings in the history of the development of AI and knowledge systems his research with Dean Allemang on how knowledge graphs can help LLMs improve the accuracy of answers of questions posed to enterprise relational databases the role of industry benchmarks in understanding the return on your invest in semantics the importance of treating semantics as a first-class citizen how business leaders can recognize and take advantage of the semantics and knowledge work that is already happening in their organizations Juan's bio Juan Sequeda is the Principal Scientist and Head of the AI Lab at data.world. He holds a PhD in Computer Science from The University of Texas at Austin. Juan’s research and industry work has been on the intersection of data and AI, with the goal to reliably create knowledge from inscrutable data, specifically designing and building Knowledge Graph for enterprise data and metadata management. Juan is the co-author of the book “Designing and Building Enterprise Knowledge Graph” and the co-host of Catalog and Cocktails, an honest, no-bs, non-salesy data podcast. Connect with Juan online LinkedIn Catalog & Cocktails podcast Video Here’s the video version of our conversation: https://youtu.be/xZq12K7GvB8 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 19. The AI pendulum has been swinging back and forth for many decades. Juan Sequeda argues that we're now at a point in the advancement of AI technology where businesses can fully reap its long-promised benefits. The key is a semantic understanding of your business, captured in a knowledge graph. Juan sees large language models as a critical enabler of this capability, in particular the ability of LLMs to accelerate the acquisition and use of valuable business knowledge. Interview transcript Larry: Hi, everyone. Welcome to episode number 19 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Juan Sequeda. Juan is the principal scientist and the head of the AI lab at data.world. He's also the co-host of the really good popular podcast, Catalog & Cocktails. So welcome, Juan. Tell the folks a little bit more about what you're up to these days. Juan: Hey, very great. Thank you so much for having me. Great to chat with you. So what am I up to now these days? Obviously, knowledge graphs is something that is my entire life of what I've been doing. This was before it was called knowledge graphs. I would say that the last year, year-and-a-half, almost two years now, I would say, is been understanding the relationship between knowledge graphs and LLMs. If people have been following our work, what we've been doing a lot has been on understanding how to use knowledge graphs to increase the accuracy for your chat with your data system, so be able to do question answering over your structured SQL databases and how knowledge graphs increase the accuracy of that. So we can chat about that. Juan:

Jesús Barrasa Over his 20-year career, Jesús Barrasa has spanned the worlds of object-oriented property graphs and assertion-based knowledge graphs. He knows as much about these two foundational technologies as anyone and offers pragmatic advice to help architects and engineers decide which approach will work best for their needs. We talked about: his role at Neo4j in which he helps companies adopt graph technology his academic study of semantic technology and his early work on mapping relational data to ontologies and enterprise ontologies his move across the graph spectrum from RDF graphs to property graphs, culminating in his current role at Neo4j his take on the similarities and differences between RDF and property graph approaches, the key commonality being linked data and the key distinction between them being the level of abstraction that they employ different ways to approach inference the origins of the semantic web and how it has made data actionable and interoperable and smarter how the professional backgrounds of software developers can affect their choice of graph technologies the crucial role of interoperability in graph technology, and our ongoing inability to productively harness it how semantics is managed and used in the property graph and RDF worlds ontology as a technology-independent way of representing knowledge the importance of staying focused on the needs of practitioners when advising them on how to make a graph technology choice how knowledge graphs can balance the "opaque power" of large language models with "explicit, declarative, explainable power" Jesús' bio Dr. Jesús Barrasa is Neo4j's AI Field CTO and the company's resident expert in Knowledge Graphs and Semantic Technologies. He co-authored the O'Reilly book "Building Knowledge Graphs: A Practitioner's Guide" (released in July 2023) and combines over 20 years of professional experience in the data management space split between industry and research and academia. Prior to joining Neo4j, Jesús worked for data integration companies like Denodo and Ontology Systems (now EXFO), where he gained first-hand experience with many successful enterprise-wide data integration deployments and large graph technology projects enhancing the operations and analytics of major companies worldwide. Jesús' doctoral work in Artificial Intelligence and Knowledge Representation focused on the automatic repurposing of legacy data as knowledge graphs. He's an active thought leader in the graph and semantics communities and co-hosts the popular monthly webcast on knowledge graphs "Going Meta." Connect with Jesús online LinkedIn Going Meta webcast. Video Here’s the video version of our conversation: https://youtu.be/7WFP_oDQsxI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 18. If you search for the term "knowledge graph," you're likely to get an equal number of results about property graphs and RDF-based graphs. Jesús Barrasa has been immersed in both of those technologies for more than 20 years. He takes a pragmatic approach to graph technology adoption, focusing on the needs of practitioners and on the ability of knowledge graphs to balance the "opaque power" of large language models with the explainable power of knowledge graphs. Interview transcript Larry: Hi everyone. Welcome to episode number 18 of the Knowledge Graph Insights Podcast. I am really delighted to welcome to the show Jesús Barrasa. Jesús is about as graph-ey a person as it gets. I got to say he has a 20-year background in this stuff. He's currently the AI field CTO, Chief Technical Officer for the graph database company Neo4j. Welcome to the show, Jesús. Tell the folks a little bit more about what you're doing these days. Jesús: Hi Larry. Thank you very much. I'm really, really glad to be here. I mean, we've been trying to plan this for a while now. Really,

Yaakov Belch Yaakov Belch is an AI researcher with strong ideas about the role of humans in AI systems. Instead of "human in the loop," he argues, we should put "humans in control." Yaakov's research looks at business contracts and how knowledge graphs and AI systems can both capture their meaning more accurately and help managers make better business decisions. We talked about: his assertion that we need humans in control, not just in the loop his research on applying AI technology to business contracts, in particular the issue of resolving inconsistencies in language model results reasons to put human concerns ahead of any particular technology the importance of having humans in control when interpreting ambiguous business decisions the importance of both accounting for business intent and asking the right questions of your data and how the loop between the two tightens over time the responsibility of human users to understand how LLMs work and to prompt and otherwise interact with them accordingly why he doesn't use the term "hallucination" when talking about LLM outputs the role and implications of applying different kinds of logic in the use of knowledge graphs an analogy that shows how the concept of a Git fork can help knowledge graph engineers account in their models for different versions of reality the real-world applications of his research, especially how the practices he is exploring can create new business value the importance of building any model off of real data and always thinking about which human needs to be in control Yaakov's bio As a mathematician and data scientist, Yaakov Belch brings a unique perspective to the world of AI and knowledge graphs. With a strong background in mathematics, including participation in prestigious International Mathematical Olympiads, Yaakov went on to earn a Ph.D. in pure mathematics from the University of Cambridge. Yaakov's career has spanned both research and industry roles. He has worked as an Algorithm Programmer, collaborating with researchers in bioinformatics and economics, co-authoring academic papers. Yaakov also served as a Senior Data Scientist at Israeli e-commerce startups, where he tackled challenges in symbolic and semantic search from different angles. Currently, Yaakov is on a sabbatical, working as an independent Data Scientist to develop his method of reliable business reasoning, precise contract understanding, and humans-in-control.ai. He sees an interesting connection between the problems from the International Mathematics Olympiads and taming the inconsistencies of large language models: "At one hand, the problems are hard and just don't open up to the known, standard methods of the field. But you know that there is a beautiful solution. You need to find the right perspective to appreciate the problem and to see that beautiful solution." Connect with Yaakov online humans-in-control.ai LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ZS9r0bfkGQc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 17. Machine learning architects often talk about the "human in the loop." Yaakov Belch thinks that when it comes to language models the right approach is to put "humans in control." Yaakov's research looks at how knowledge graphs and large language models can help put humans in control of business contracts, capturing the actual intent that underlies them and facilitating better business decision-making based on the discoveries that they enable. Interview transcript Larry: Hi everyone. Welcome to episode number 17 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yaakov Belch. Yaakov is an independent senior data scientist and he’s made this really provocative statement about … There’s all this talk in the AI world in general about humans in the loop. And Yaakov says, “No.

Michael Iantosca Where content, knowledge management, and AI converge, you'll find Michael Iantosca. As many in the AI world flock to probabilistic models like LLMs, Michael takes a deterministic approach to content management and knowledge engineering, using ontologies and knowledge graphs to ground content in a concrete facts. This approach embodies his insight that content and the models that describe it are not static information but rather valuable, ever-evolving enterprise IP assets. We talked about: his 44-year career in content, knowledge management and localization/globalization roles the three pillars of his work: content, knowledge management, and engineering the need he sees in his work to move away from probabilistic, vector-based models to deterministic, neuro-symbolic models like knowledge graphs how he decides which models are appropriate to use with each of the varied kinds of data he works with his explorations of how to automatically construct a knowledge graph to use to power generative AI solutions how he acquires and develops ontology skills in his team how graph technology supports the "total content experiences" he builds how the non-static nature of content makes it a poor candidate to be managed in a static system like a vector-based model the relative merits and utility of 1) deterministic retrieval for structured content and 2) probabilistic retrieval for unstructured content the power of combining content models, knowledge models, and ontologies and how they can become crucial enterprise IP assets his belief that we are entering a golden age of content and knowledge engineering Michael's bio Michael Iantosca is the Senior Director of Knowledge Platforms and Engineering at Avalara, a sales tax automation company. With over four decades of leadership in technical content management, Michael has been a pioneer in advancing the profession, driving innovations in structured content, intelligent authoring, and scalable knowledge platforms. Renowned for bridging engineering and content teams, he has championed the adoption of AI and cutting-edge technologies to enhance user experience. A thought leader and mentor, Michael continues to shape the future of technical communication through his expertise and passion for innovation. Connect with Michael online LinkedIn Medium ThinkingDocumentation Video Here’s the video version of our conversation: https://youtu.be/WG9Nl5OY3QI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 16. A lot of work in the AI world these days is about vectorizing giant collections of static, unstructured content and data for LLMs. Michael Iantosca has worked for decades in a world where content is dynamic, always precisely structured, and contextualized with rich metadata. So he has a different take on architectural innovations like graph RAG, favoring knowledge-based deterministic retrieval of content over vector-based models and probabilistic methods. Interview transcript Larry: Hi, everyone. Welcome to episode number 16 of the knowledge graph Insights podcast. I am really delighted today to welcome to the show, Michael Iantosca. Michael is currently the Senior Director of Knowledge Platforms and Engineering at Avalara, the big tax-compliance automation software company. He's also got a long history. He's spent a couple of decades, a few decades at IBM prior to his role at Avalara. Welcome to the show, Michael. Tell the folks a little bit more about what you're up to these days. Michael: Larry, thank you for having me. It's a pleasure and an honor to get a few minutes to talk to you today. Yeah, I have just started my 44th year primarily in the professional content space, but also in the knowledge management and localization globalization space as well. I have been involved with content since the early days of SGML that began the structured content revolution and wo...

Fran Alexander When Fran Alexander looks at the current AI landscape she sees some interesting parallels between the Alien vs Predator science fiction franchise and the way RAG and other architectures are combining LLMs and knowledge graphs. We talked about: the analogy she draws between the Alien and Predator science fiction franchise with LLMs and knowledge graphs how the human-esque (if malevolent) cognitive and behavioral nature of Predators aligns more with knowledge graphs and how the unpredictable and stochastic nature of Aliens aligns more with LLMs how the eloquence of LLM outputs can deceive humans the lack of explainability and transparency in both Alien and LLM behavior, and the opposite in knowledge graphs the difficulty of dealing with baked-in biases in LLMs the lack of repeatability in LLMs and the opposite in KGs the current trend of architectures and practices like RAG that draw on the strengths of KGs and LLMs to get better results, just as the Alien and Predator media franchises combined forces how over the past year or so investment in LLMs has overshadowed all other investments, just as Aliens are out to wipe out anything that's not an Alien her approach to AI architectures that combine LLMs and knowledge graphs how different kinds of people consume LLM output how she helps enterprise decision makers choose whether to address a use case with a knowledge graph or an LLM how taxonomists and ontologists can use LLMs in their work the Alien Loves Predator UK Facebook group and Alien and Predator on a seesaw Alien and Predator cosplay actors on a seesaw Fran's bio Fran started her career as a writer and editor of dictionaries and thesauruses in the UK, and, as technology evolved, she specialised in information architecture, search systems, and digital archives, and more recently, the use of semantics in knowledge graphs and LLM applications. Having worked on reference publications including the Collins English Dictionary, and as Taxonomy Manager for the BBC Archive, she now lives in Montreal, Canada, and is the Senior Taxonomist for Expedia Group. She was Taxonomy Bootcamp London’s Taxonomy Practitioner of the Year 2023. Connect with Fran online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/VWwDBIws6G8 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 15. When two impressive domains converge, amazing things can happen. When the Alien and Predator science fiction franchises joined forces, both enjoyed new commercial success. Similarly, in the AI world right now, Fran Alexander sees knowledge graphs and large language models combining forces to create retrieval augmented generation and similar architectures that work together to create systems more useful and valuable than the sum of their individual capabilities. Interview transcript Larry: Hi everyone. Welcome to episode number 15 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Fran Alexander. Fran is an independent taxonomist and ontologist based in Montreal. And so, welcome Fran. Tell the folks a little bit more about what you're up to these days. Fran: Hi Larry. Well, it's nice to talk to you again. I really enjoyed talking to you on the previous podcast that we did a little while ago. And that one was kind of a bit of a general introduction to taxonomies, ontologies, thesauruses, knowledge modeling and semantics. But this time, I thought we could talk about knowledge graphs and LLMs. They're a big hot topic and I did a presentation earlier on in the year for Taxonomy Boot Camp London, Bite-sized Taxonomy Boot Camp London. That was a lot of fun and has been really popular. A lot of people have been asking me about it. I've revisited it a couple of times and that's LLMs versus knowledge graphs, Alien versus Predator. Larry: Okay. So why Alien versus Predator?

Andreas Blumauer Every enterprise nowadays is awash in data, content, and knowledge, the understanding of which is all too often available only in employees' heads. Forward-thinking businesses are moving to knowledge graphs to capture that tacit knowledge so that they can better understand and use it. Andreas Blumauer shows how those graphs work best when they're accompanied by a domain knowledge model, creating a "semantic layer" that provides a vivid map of your business knowledge. We talked about: the recent merger of his company, Semantic Web Company, with Ontotext to form a new company, Graphwise, which aims to help enterprises build their semantic layer the 20-year-old origin story behind his definition and description of a semantic layer the emerging trend he sees of data, content, and knowledge people coming together, often around explorations of language and content structure how getting domain knowledge out of people's heads and into multimodal AI architectures can streamline business research how a semantic layer provides a map of your business knowledge the importance of a domain knowledge model in a semantic layer architecture the inadequacy of "desktop data integration," the practice of calling colleagues, consulting varied sources, and otherwise searching through ad hoc enterprise knowledge sources, and the stress it can cause how a domain knowledge model can connect and reveal knowledge across an organization the surprisingly small footprint of the domain knowledge model in an enterprise knowledge graph, typically just 1% or so of the semantic layer how LLMs can help in the discovery of data and the construction of knowledge graphs the two main elements of the semantic layer: domain knowledge models (taxonomies, ontologies, etc.) and an automatically generated enterprise knowledge graph the different perceptions of the value of the semantic layer across data and content professionals, and how the arrival of gen AI has resulted in them talking together more to each other how the semantic layer can facilitate the alignment of vocabularies and the understanding of data across business divisions the benefits of a hybrid centralized-decentralized/global-local "glocalization" semantic layer strategy Andreas' bio Andreas Blumauer is SVP Growth at Graphwise, and CEO and founder of the Semantic Web Company (SWC), provider and developer of the PoolParty Semantic Platform and leading solution provider in the field of semantic AI and RAG. For more than 20 years, he has worked with more than 200 organizations worldwide to deliver AI and semantic search solutions, knowledge platforms, content hubs and related data modeling and integration services. Most recently, Andreas has been involved in the development of AI-powered ESG solutions for companies and investors. A globally recognized thought leader and author in the field of semantic AI and graph technologies, Andreas has helped define and implement knowledge and AI strategies for various industries and domains. He is the author of "The Knowledge Graph Cookbook: Recipes that Work", a practical guide to building and deploying knowledge graphs in organizations. He is passionate about enabling clients to harness the power of semantic AI and graph technologies to achieve their business goals and make a social contribution. Since 2022, Andreas has focused primarily on developing AI-based solutions that help organizations implement ESG and sustainable systems. Connect with Andreas online LinkedIn Graphwise Resources mentioned in this episode Knowledge Graphs, LLMs and Semantic AI LinkedIn group From Data to Trust: Leveraging Knowledge Graphs for Enterprise AI Solutions webinar Enterprise architecture model that includes a semantic layer Video Here’s the video version of our conversation: https://youtu.be/dEQh6m6zoDE Podcast intro transcript